Biological-Age Clocks

MRI-based brain age is a biological-age estimate derived from structural or functional brain imaging features—including cortical thickness, white-matter integrity, grey-matter volume and functional connectivity—processed by machine-learning models trained on large neuroimaging cohorts. The gap between predicted brain age and chronological age, termed the brain-age gap or BrainAGE (introduced by Franke et al., 2010), is a biomarker of brain health: a positive gap (brain appearing older) associates with cognitive decline, neurodegeneration, stroke and all-cause mortality, while a negative gap is linked to better cognitive reserve. Accuracy and interpretability vary with imaging protocol, preprocessing pipeline and training cohort demographics.

CausAge is an epigenetic-age clock introduced by Ying, Gladyshev and colleagues (preprint 2022; Nature Aging 2024) that attempts to address a fundamental limitation of correlation-trained clocks: standard elastic-net or regression clocks select CpG sites associated with age without distinguishing whether the methylation change causes, results from, or merely co-varies with the ageing process. CausAge applies Mendelian-randomization-informed causal inference to identify CpGs whose methylation change is more likely upstream of biological ageing, and separately derives DamAge (damage-associated accelerated ageing) and AdaptAge (adaptive response) sub-clocks. The framework suggests that clock CpGs are heterogeneous in their causal role and that mortality-associated acceleration may be driven primarily by damage-linked sites rather than adaptive ones.

The DNAm Skin & Blood clock, published by Horvath and colleagues in 2018, is an epigenetic age estimator based on 391 CpG sites selected from methylation arrays applied to skin fibroblasts and blood samples. It was developed partly to overcome the observation that the original 2013 Horvath clock systematically underestimated age in keratinocytes and fibroblasts, tissues central to studies of in-vitro reprogramming and rejuvenation. Because it was trained on a tissue type directly accessible in longevity intervention studies, it is frequently used as a readout in partial reprogramming experiments alongside the multi-tissue Horvath clock.

DunedinPACE (Pace of Aging Calculated from the Epigenome) is an epigenetic clock published in 2022 by Belsky and colleagues that estimates the rate of biological ageing rather than a static age. It was trained in the Dunedin 1972-1973 birth cohort on longitudinal change across 19 organ-system biomarkers and translated into a DNA-methylation score using 173 CpGs. The score is calibrated so that 1 represents the cohort-mean pace of one year of biological aging per chronological year; values above 1 indicate faster-than-average ageing. DunedinPACE shows good test-retest reliability and predicts morbidity and mortality.

Epigenetic age is a biological-age estimate derived from DNA-methylation patterns at selected CpG sites, computed by algorithms known as epigenetic clocks (e.g. Horvath, Hannum, PhenoAge, GrimAge, DunedinPACE). The difference between epigenetic and chronological age, called epigenetic age acceleration, is associated with mortality, cardiovascular disease and cancer in research cohorts. Validation depends on the specific clock; first-generation clocks track chronological age, while mortality-trained clocks better predict health outcomes. Commercial consumer tests vary in reliability.

GlycanAge is a biological-age estimate derived from the N-glycan composition of immunoglobulin G (IgG), measured in blood plasma by high-throughput capillary electrophoresis or ultra-performance liquid chromatography. IgG glycosylation shifts predictably with age—specifically, a decline in galactosylation and sialylation alongside a rise in bisecting GlcNAc accompanies the shift toward a more pro-inflammatory IgG glycome—and these patterns also respond to lifestyle interventions and chronic disease. Because glycans regulate IgG effector function and inflammaging, the measure captures an immunologically relevant dimension of ageing not directly accessible to DNA-methylation clocks; however, reference populations and clinical thresholds are still under active investigation.

GrimAge is a second-generation epigenetic clock introduced by Lu et al. (2019, with Steve Horvath as senior author). Instead of predicting chronological age, it is trained on time-to-death and combines DNA-methylation surrogates for seven plasma proteins (e.g. PAI-1, GDF-15) and DNAm-based smoking pack-years. In multiple cohorts GrimAge and the updated GrimAge2 (2022) outperform earlier clocks at predicting all-cause mortality, cardiovascular disease and cancer. It is widely used in research; clinical use as a diagnostic endpoint remains investigational.

The Hannum clock is a blood-based epigenetic age estimator published by Gregory Hannum and colleagues in 2013. It uses DNA methylation levels at 71 CpG sites, derived from whole-blood samples of 656 individuals, to predict chronological age with a cross-validated correlation of ~0.96. Unlike the Horvath clock, which is multi-tissue, the Hannum clock was trained and validated specifically in blood, making it less generalisable to other tissues. It remains widely cited but has been largely superseded for mortality prediction by second-generation clocks trained on health outcomes.

The Horvath clock is a multi-tissue epigenetic age estimator published by Steve Horvath in 2013. It uses DNA methylation levels at 353 CpG sites to predict chronological age across more than 50 tissues and cell types with a median error of about 3.6 years. It is the most-cited epigenetic clock and well validated as a predictor of chronological age, but its association with mortality and disease is weaker than that of later, mortality-trained clocks such as GrimAge.

iAge is an inflammatory-age metric introduced by Sayed and colleagues (2021, Stanford) that uses a deep-learning model trained on a panel of 50 circulating cytokines and chemokines from 1,001 healthy individuals across the decade-long Stanford 1000 Immunomes Project. The model compresses the immunome profile into a single inflammatory-age score that predicts cardiovascular risk, multimorbidity, and all-cause mortality independently of chronological age. CXCL9—a chemokine associated with T-cell recruitment and endothelial dysfunction—was identified as the single most informative driver. The clock highlights the immune system as a distinct, targetable dimension of biological ageing.

PCGrimAge applies the same principal-component denoising framework introduced by Higgins-Chen et al. (2022) to the GrimAge methylation features, producing a more technically stable version of one of the strongest mortality-predictive epigenetic clocks. By regressing out array-platform and batch variance before scoring, PCGrimAge shows markedly better within-individual reproducibility than the original GrimAge algorithm (Lu et al. 2019), an important property when tracking biological-age change in response to interventions such as caloric restriction, exercise, or pharmacological treatments. Like PCPhenoAge, it represents a methodological advance rather than a new biological model.

PCPhenoAge is a technically refined variant of DNAm PhenoAge introduced by Higgins-Chen and colleagues (2022) that applies principal-component (PC) regression to the underlying CpG data before computing the age score. The PC transformation removes technical noise and batch effects that inflate variance in standard methylation arrays, yielding a clock with substantially improved test-retest reliability and reduced sensitivity to sample quality or preprocessing pipeline. In practice, PCPhenoAge retains the mortality-predictive validity of its predecessor while being more suitable for longitudinal studies and intervention trials where within-individual change is the primary outcome.

PhenoAge is a composite biological-age measure developed by Levine and colleagues in 2018. The original blood-based version combines nine clinical biomarkers including albumin, creatinine, glucose, C-reactive protein and white blood cell count with chronological age, calibrated against mortality. A DNA-methylation version, DNAm PhenoAge, transfers the score to epigenetic data. PhenoAge predicts all-cause mortality and multimorbidity better than chronological age and has been validated in several large cohorts, although clinical use is still emerging.

ProAge and related proteomic-age clocks estimate biological age from the concentrations of hundreds to thousands of plasma or serum proteins measured by aptamer-based (SomaScan) or proximity-extension assay (Olink) platforms. Landmark studies by Lehallier and colleagues (2019, Nature Medicine) demonstrated that the plasma proteome changes non-linearly with age in three distinct waves, and subsequent work trained predictive models on up to ~3,000 proteins. Proteomic clocks capture post-transcriptional and secreted signals not reflected in DNA methylation, and recent analyses suggest protein-based age acceleration associates with age-related disease risk, though platform-specific protein selection means scores are not directly interchangeable across studies.

RetinaAge is a biological-age clock derived from fundus photographs of the retina, using deep-learning models trained to predict chronological age from the vascular and neural features of the optic disc, fovea, and retinal vasculature. Published by Zhu and colleagues (2022, British Journal of Ophthalmology), the model was trained on fundus images from healthy UK Biobank participants and applied to the broader UK Biobank cohort (~80,000 images, ~46,000 participants); the gap between predicted retinal age and chronological age independently predicted all-cause mortality and cardiovascular events. Because the retina shares embryological origin with the central nervous system and is the only site where microvasculature can be imaged non-invasively, it offers a clinically practical window onto systemic and neural vascular aging.

SystemsAge is a multi-organ biological-age framework introduced by Tian and colleagues (2023, Nature Medicine) that uses routine clinical laboratory data to generate organ-specific age estimates across 3 brain systems (cerebral cortex, thalamus, cerebellum) and 7 body systems (cardiovascular, respiratory, renal, gastrointestinal, liver, musculoskeletal, immune) — 10 systems total — from blood-based biomarker panels. Rather than a single composite score, it produces a profile of organ biological ages, allowing detection of discordant ageing across systems. In the UK Biobank, organ age gaps predicted organ-specific disease and all-cause mortality; individuals whose organ ages were globally younger than their chronological age showed lower mortality risk. The approach is attractive for scalable population studies because it relies on standard blood tests rather than DNA methylation arrays or imaging. A separate methylation-based 'SystemsAge' clock by Sehgal et al. 2025 Nature Aging (Levine lab) covers 11 physiological systems via a single blood-based assay; the two clocks share a name but use different inputs.